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関連する概念動画

Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Discrete-time Fourier transform01:26

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Dynamic Equilibrium02:20

Dynamic Equilibrium

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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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前頭皮質の持続的な活動に 引き寄せのダイナミクスが根底にある

Hidehiko K Inagaki1, Lorenzo Fontolan1, Sandro Romani2

  • 1Janelia Research Campus, HHMI, Ashburn, VA, USA.

Nature
|February 8, 2019
PubMed
まとめ
この要約は機械生成です。

短期記憶は 過去の感覚と将来の行動を 結びつけるのに不可欠で 持続的な神経活動によって支えられています この研究は,マウスの前側運動皮質 (ALM) の離散的アトラクターダイナミクスを明らかにし,この運動計画記憶の基礎となっています.

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科学分野:

  • 神経科学
  • 計算神経科学
  • モーター コントロール

背景:

  • 短期記憶は 過去の感覚に基づいて 将来の行動を可能にします
  • 持続的な神経活動は 短期記憶と運動計画の特徴です
  • 前側運動皮質 (ALM) のニューロンは,遅延応答のタスク中に持続的な活動を表します.

研究 の 目的:

  • 運動計画のための短期記憶における持続的な神経活動の基礎原理を解明する.
  • 遅延応答タスク中のALMにおけるニューロン集団の動態を調査する.
  • 誘導体のダイナミクスが 運動計画における短期記憶を 支配するかどうかを判断する.

主な方法:

  • マウスの細胞内および細胞外電気生理学を組み合わせた.
  • 神経活動を操作するために 光遺伝学的な混乱を利用した
  • ニューラルダイナミクスを分析するために ネットワークモデリングを使用した.
  • 前側運動皮質 (ALM) のニューロンから記録されています.

主要な成果:

  • 遅延期間中,ALMニューロンの活動は,特定の運動方向に対応する離散的な"エンドポイント"へと進化した.
  • これらの特定されたエンドポイントは,一時的な光遺伝的障害に対する強度を示した.
  • 時には,乱れが他のエンドポイントに状態のスイッチを誘導し,誤ったアクションにつながります.
  • 証拠は,アトラクターダイナミクスが観察された持続的な活動を支配することを示唆しています.

結論:

  • 分離型アトラクターダイナミクスは 運動計画における短期記憶に不可欠です
  • ALMネットワークは,これらのアトラクタを使用して,時間とともにアクションに関連する情報を保持します.
  • このメカニズムは強度を提供し 状態の切り替えを可能にし 行動の結果に影響を与えます